Deep Learning Based Single-Channel Blind Separation of Co-frequency Modulated Signals

  • Chen Chen
  • Zhufei Lu
  • Zhe Guo
  • Feng YangEmail author
  • Lianghui Ding
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 312)


This paper presents our results in deep learning (DL) based single-channel blind separation (SCBS). Here, we propose a bidirectional recurrent neural network (BRNN) based separation method which can recover information bits directly from co-frequency modulated signals after end-to-end learning. Aiming at the real-time processing, a strategy of block processing is proposed, solving high error rate at the beginning and end of each block of data. Compared with the conventional PSP method, the proposed DL separation method achieves better BER performance in linear case and nonlinear distortion case with lower computational complexity. Simulation results further demonstrate the generalization ability and robustness of the proposed approach in terms of mismatching amplitude ratios.


Single-channel blind separation (SCBS) Deep learning (DL) Bidirectional recurrent neural network (BRNN) 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Yichang Testing Institute of Technology ResearchYichangChina
  3. 3.Shanghai Microwave Research Institute and CETC Key Laboratory of Data Link TechnologyShanghaiChina

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